Beautiful networkx graph

draw_nx_beautiful.py

import copy
import networkx
import matplotlib.pyplot as plt

# Generate a graph.
# Here I chose an ER graph.
g = nx.erdos_renyi_graph(20, 0.3)

# Get positions.
# Here I use the spectral layout and add a little bit of noise.
pos = nx.layout.spectral_layout(g)
pos = nx.spring_layout(g, pos=pos, iterations=50)

# Create position copies for shadows, and shift shadows
pos_shadow = copy.deepcopy(pos)
shift_amount = 0.006
for idx in pos_shadow:
    pos_shadow[idx][0] += shift_amount
    pos_shadow[idx][1] -= shift_amount

#~~~~~~~~~~~~
# Draw graph
#~~~~~~~~~~~~
fig = plt.figure(frameon=False)
ax = fig.add_axes([0, 0, 1, 1])
ax.axis('off')

nx.draw_networkx_nodes(g, pos_shadow, node_color='k', alpha=0.5)
nx.draw_networkx_nodes(g, pos, node_color="#3182bd", linewidths=1)
nx.draw_networkx_edges(g, pos, width=1)

This is awesome!

Note: This emulates the graphing style of The Nature Of Computation by Cristopher Moore and Stephan Mertens.

Nice example!